scholarly journals ESTIMATES OF GENETIC PARAMETERS USING RELM/BLUP FOR INTRA-POPULATIONAL GENETIC BREEDING OF Platonia insignis Mart.

2016 ◽  
Vol 40 (3) ◽  
pp. 561-573 ◽  
Author(s):  
Maria Clideana Cabral Maia ◽  
Luciano Medina Macedo ◽  
Lúcio Flavo Lopes Vasconcelos ◽  
João Pedro Alves Aquino ◽  
Luís Cláudio Oliveira ◽  
...  
Keyword(s):  
Variance Components ◽  
Genetic Parameters ◽  
Recurrent Selection ◽  
Indirect Selection ◽  
Best Linear Unbiased Prediction ◽  
Selection Differential ◽  
Linear Unbiased Prediction ◽  
Genetic Breeding ◽  
Best Linear Unbiased ◽  
Selection Of

ABSTRACT The aim of this study was to estimate genetic parameters to support the selection of bacuri progenies for a first cycle of recurrent selection, using the REML/BLUP (restricted maximum likelihood/best linear unbiased prediction) procedure to estimate the variance components and genotypic values. Were evaluated twelve variables in a total of 210 fruits from 39 different seed trees, from a field trial with an experimental design of incomplete blocks with clonal replies among subplots. The three variables related with the fruit development (weight, diameter, length) showed strong correlation, and where fruit length showed higher heritability and potential to be used for indirect selection. Among the 39 progenies evaluated in this study, five present potential to compose the next cycle of recurrent selection, due they hold good selection differential either to agrotechnological variables as to development of bacuri fruit.

Genetic Parameters and Selection in Full-Sib Families of Tall Fescue Using Best Linear Unbiased Prediction (BLUP) Analysis of Animal Breeding and Genetic

2021 ◽  
Author(s):  
Fatemeh Pirnajmedin ◽  
Mohammad Mahdi Majidi ◽  
Mohammad Hadi Taleb ◽  
Davoud Rostami
Keyword(s):  
Ssr Markers ◽  
Tall Fescue ◽  
Genetic Parameters ◽  
Best Linear Unbiased Prediction ◽  
Forage Yield ◽  
Linear Unbiased Prediction ◽  
Breeding Programs ◽  
Sib Families ◽  
Best Linear Unbiased ◽  
Selection Of

Abstract Background: Better understanding of genetic structure of economic traits is crucial for identification and selection of superior genotypes in specific breeding programs. Best linear unbiased prediction (BLUP) is the most efficient method in this regards, which is poorly used in forage plant breeding. The present study aimed to assess genetic variation, estimate genetic parameters, and predict breeding values of five essential traits in full sib families (recognized by EST-SSR markers) of tall fescue using REML/BLUP procedure. Method: Forty-two full-sib families of tall fescue (included of 120 individual genotypes), recognized by EST-SSR markers’ along with twenty-one their corresponding parental genotypes were assessed for biomass production and agro-morphological traits at three harvests (spring, summer, and autumn) in the field during 4 years (2017-2020). Results: Considerable genotypic variability was observed for all traits. Low narrow-sense heritability (h2n) for dry forage yield (DFY) at three harvest indicates that non-additive gene actions may play an important role in the inheritance of this trait. Higher h2n of yield related traits and flowering time and also significant genetic correlation of these traits with forage yield, suggests that selection based on these traits via developing an index may lead to indirect genetic improvement of DFY. Conclusion: Our results showed the adequacy of REML/BLUP procedure for identification and selection of preferable parental genotypes and progenies with higher breeding values for future breeding programs such as variety development in tall fescue. Parental genotypes 21M, 1M, and 20L were identified as superior and stable genotypes and could also produce the best hybrid combinations when they were mostly used as maternal parent.

scholarly journals Prediction of genotypic values and estimation of genetic parameters in common bean

2008 ◽  
Vol 51 (3) ◽  
pp. 465-472 ◽  
Author(s):  
Alisson Fernando Chiorato ◽  
Sérgio Augusto Morais Carbonell ◽  
Luiz Antônio dos Santos Dias ◽  
Marcos Deon Vilela de Resende
Keyword(s):  
Common Bean ◽  
Genetic Parameters ◽  
High Accuracy ◽  
Best Linear Unbiased Prediction ◽  
Broad Sense Heritability ◽  
Phaseolus Vulgaris L ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Estimation Of Genetic Parameters ◽  
Selection Of

Eighteen common bean (Phaseolus vulgaris L.) genotypes were evaluated in 25 environments of the state of São Paulo in 2001 and 2002. The estimation of genetic parameters by the Restricted Maximum Likelihood (REML) and the prediction of genotypic values via Best Linear Unbiased Prediction (BLUP) were obtained by software Selegen-REML/BLUP. The estimate of the broad-sense heritability was low for the grain yield (0.03), since it took individual plots into consideration and was free of the effects of interaction with years, cultivation periods and site. Nevertheless, the heritability at the level of line means across the various environments was high (0.75), allowing a high accuracy (0.87) in the selection of lines for planting in the environment mean. Among the 18 genotypes, the predicted genotypic values of nine were higher than the general mean. The genetic gain predicted with the selection of the best line, in this case line Gen 96A31 of the IAC, was 16.25%.

Combined selection of progeny in crop breeding using best linear unbiased prediction

2012 ◽  
Vol 92 (3) ◽  
pp. 553-562 ◽  
Author(s):  
José Marcelo Soriano Viana ◽  
Vinícius Ribeiro Faria ◽  
Fabyano Fonseca e Silva ◽  
Marcos Deon Vilela de Resende
Keyword(s):  
Least Squares ◽  
Best Linear Unbiased Prediction ◽  
Pedigree Information ◽  
Crop Breeding ◽  
Additive Variance ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Combined Selection ◽  
Selection Of ◽  
Unbiased Prediction

Viana, J. M. S., Faria, V. R., Fonseca e Silva, F. and Vilela de Resende, M. D. 2012. Combined selection of progeny in crop breeding using best linear unbiased prediction. Can. J. Plant Sci. 92: 553–562. Combined selection is an important strategy in crop breeding. As the classical index does not consider pedigree information, the objective of this study was to evaluate the efficiency of the best linear unbiased prediction (BLUP) methodology for combined selection of progeny. We analyzed expansion volume (EV) and grain yield of parents and inbred and non-inbred progeny from the popcorn population Viçosa. The BLUP analyses, single-trait and of the same character measured in parents and progeny (combined parent-family) were performed using the ASReml software. Because the experiments were balanced, the estimates of the additive variance from the BLUP and least squares analyses were generally equivalent. The accuracies of the BLUP analyses do not clearly establish the superior technique. The accuracy of the classical index tended to be higher than that obtained from BLUP analyses. There was equivalence between BLUP and least squares analyses relative to half-sib and inbred progeny selection, and superiority of the combined parent-family BLUP index for full-sib selection. The BLUP analyses also differed from the least squares analysis on the coincidence of selected parents. The populations obtained by selection based on BLUP of breeding values presented a lower effective size.

EVALUATION OF BEEF SIRES ACROSS BREEDS FOR CALVING EASE

1977 ◽  
Vol 57 (4) ◽  
pp. 635-645 ◽  
Author(s):  
L. R. SCHAEFFER ◽  
J. W. WILTON
Keyword(s):  
Maximum Likelihood ◽  
Scoring System ◽  
Variance Components ◽  
Error Variance ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Calving Ease ◽  
Best Linear Unbiased ◽  
Maximum Likelihood Prediction ◽  
Unbiased Prediction

Agriculture Canada and Alberta Record of Performance calving ease records on 54,139 calves from 3,338 sires of 18 breeds were used to evaluate sires by comparisons across breeds of sire. An objective scoring system was applied to the calving ease codes to derive appropriate weights for each category rather than using percentage of unassisted births or assuming equal intervals between categories. Common sire and error variance components were assumed for all breeds of sire. Heritability of calving ease under the model used was estimated to be.10 by maximum likelihood. Prediction of sire values for calving ease scores of future calves were calculated by best linear unbiased prediction procedures. Shorthorn, Hereford, and Angus sires caused relatively few calving difficulties, while Maine-Anjou sires caused more difficulties. Age of dam and sex of calf differences were also important. The range of sire evaluations for calving ease was narrow, but the bulls in either extreme could be identified.

scholarly journals Best linear unbiased prediction for genetic evaluation in reciprocal recurrent selection with popcorn populations

2013 ◽  
Vol 152 (3) ◽  
pp. 428-438
Author(s):  
J. M. S. VIANA ◽  
G. B. MUNDIM ◽  
R. O. DELIMA ◽  
F. F. E SILVA ◽  
M. D. V. DE RESENDE
Keyword(s):  
Zea Mays ◽  
Combining Ability ◽  
Recurrent Selection ◽  
Best Linear Unbiased Prediction ◽  
Unbiased Estimation ◽  
Reciprocal Recurrent Selection ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased Estimation ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

SUMMARYThe objective of the present study was to present the theory and application of best linear unbiased prediction (BLUP) in reciprocal recurrent selection (RRS). Seven progeny tests from two RRS programmes with popcorn (Zea mays L. ssp. mays [syn. Zea mays L. ssp. everta (Sturtev.) Zhuk.]) populations were conducted and analysed for expansion volume and grain yield. The interpopulation half- and full-sib family models were fitted using ASReml software. Half-sib selection is equivalent to selection for the general combining ability (GCA) of the common parents. With inbred full-sib progeny and BLUP analysis, it is possible to predict the general and specific combining ability effects. The standard error of prediction of the progeny effect was lower than the standard deviation of the best linear unbiased estimation (BLUE) estimate. For half- and full-sib RRS, the BLUE and BLUP provided highly correlated estimates of progeny genotypic values. The coincidence between selected parents ranged from 64 to 95%. With inbred full-sib progeny, the correlations between the BLUE of progeny genotypic values and the BLUP of GCA effects were lower. Consequently, the coincidence between selected parents was lower, ranging from 0 to 57%. The percentage of common selected inbred progeny based on the BLUE and BLUP of the progeny genotypic value ranged from 57 to 100%.

scholarly journals Genetic progress in maize from advanced cycles of reciprocal recurrent selection through REML/BLUP

2020 ◽  
pp. 1295-1301
Author(s):  
José Arantes Ferreira Júnior ◽  
Gabriel Moreno Bernardo Gonçalves ◽  
Jocarla Ambrosim Crevelari ◽  
Julio Cesar Fiorio Vettorazzi ◽  
Vivane Mirian Lanhellas Gonçalves ◽  
...  
Keyword(s):  
Grain Yield ◽  
Mixed Models ◽  
Variance Components ◽  
Genetic Parameters ◽  
Recurrent Selection ◽  
Yield Potential ◽  
Reciprocal Recurrent Selection ◽  
Genetic Progress ◽  
Advanced Cycles ◽  
Selection Of

Breeding methods such as reciprocal recurrent selection (RRS) is very important alternative to breeders. This strategy, besides allowing continuous genetic progress, helps maintaining the genetic variability of populations. The aim of the present study was to estimate genetic parameters, as well as to predict selection gains in advanced cycles of reciprocal recurrent selection of maize progenies through mixed models. Two selection cycles were considered. We evaluated 196 and 169 progenies in the 15 and 16 cycles, respectively. The yield potential of the progenies was evaluated from the following characteristics: grain yield, number of ears, weight of ears and weight of 100 seeds. The estimate of variance components and the prediction of genetic values were based on the REML/BLUP method. Overall, the genetic parameters were increased in the 16th cycle estimates compared to the previous cycle. It is worth highlighting that heritability coefficient for the grain yield was (0.81) in the 16th cycle, since it resulted in 21% of selection gain. Results showed that, even after sixteen recurrent selection cycles, the populations under selection have high potential for satisfactory genetic gains.

Use of the REML/BLUP methodology for the selection of sweet orange genotypes

2021 ◽  
Vol 56 ◽  
Author(s):  
Márcia da Costa Capistrano ◽  
Romeu de Carvalho Andrade Neto ◽  
Vanderley Borges dos Santos ◽  
Lauro Saraiva Lessa ◽  
Marcos Deon Vilela de Resende ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Sweet Orange ◽  
Superior Performance ◽  
Yield Potential ◽  
Components Of Variance ◽  
Linear Unbiased Prediction ◽  
Character Set ◽  
Best Linear Unbiased ◽  
Fruit Mass ◽  
Selection Of

Abstract: The objective of this work was to select superior sweet orange (Citrus sinensis) genotypes with higher yield potential based on data from eight harvests, using the residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology. The experiment was carried out from 2002 to 2008 and in 2010 in the municipality of Rio Branco, in the state of Acre, Brazil. Analyzes of deviance were performed to test the significance of the components of variance according to the random effects of the used model, and parameters were estimated from individual genotypic and phenotypic variances. A selection intensity of 20% was adopted regarding genotypic selection, i.e., only the best 11 of the 55 genotypes tested were selected. The estimates of the genetic parameters show the existence of genetic variability and the selection potential of the studied sweet orange genotypes. The genotypic correlation between harvests is of low magnitude, except for the variable average fruit mass, and, as a reflex, there is a change in the ordering of the genotypes. Genotypes 5, 48, 19, 14, and 47 stand out as being the most productive, and, therefore, are the most suitable for selection purposes. Genotypes 14 and 47 show superior performance for the character set evaluated.

A comparison of alternative nucleus breeding systems and a sire referencing scheme for sheep improvement

1996 ◽  
Vol 62 (2) ◽  
pp. 265-270 ◽  
Author(s):  
J. A. Roden
Keyword(s):  
Selection Response ◽  
Best Linear Unbiased Prediction ◽  
Breeding Systems ◽  
Linear Unbiased Prediction ◽  
Sequential Selection ◽  
Best Linear Unbiased ◽  
The Mean ◽  
Nucleus Breeding ◽  
Open Nucleus ◽  
Selection Of

AbstractStochastic simulation was used to compare selection response and rate of inbreeding in four nucleus breeding systems and a sire referencing scheme for sheep: an open nucleus system (ONS), an open nucleus system with sequential selection of the nucleus (ONS-S), a sire referencing scheme (SRS) and a dispersed open nucleus system (DONS). Selection was based on best linear unbiased prediction of breeding values for a single trait measurable on all individuals prior to selection. Selection in a population of 1200 ewes equally divided into 10 flocks was simulated over a 15-year period. The mean rate of genetic gain was proportionately about 0-15 higher in ONS-S and DONS compared with ONS and SRS. The rate of inbreeding in SRS was considerably lower and in ONS-S, considerably higher, than in the other systems. The level of prolificacy in the population did not influence the relative ranking of the breeding systems but may have implications for their optimal structure.

scholarly journals Selection of forage oat genotypes through GGE Biplot and BLUP

2020 ◽  
Author(s):  
Franklin Santos ◽  
y Félix Marza
Keyword(s):  
Statistical Methods ◽  
Dry Matter ◽  
Best Linear Unbiased Prediction ◽  
Dry Matter Yield ◽  
Gge Biplot ◽  
Average Yield ◽  
Phenotypic Stability ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Selection Of

AbstractIn Bolivia, there is a low predominance of forage oat productivity. Therefore, it was proposed to select more productive and stable genotypes through statistical methods of GGE Biplot and BLUP. The research was conducted in three environments in Bolivia and six commercial varieties of forage oats were evaluated; three of them correspond to INIA Peru and the rest of Bolivia. Data were analyzed through GGE Biplot and BLUP (Best Linear Unbiased Prediction) and an average yield of 10.29 ±3.51 t ha−1 of dry matter was obtained. BLUP accumulated greater variance than GGE Biplot in the first two components. In terms of productivity and stability values, both models have the same selection trend. Thus, Tayco and Texas were selected for their outstanding characteristic in dry matter yield and phenotypic stability.

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